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Scaling Memcache at Facebook Scaling Memcache at Facebook Rajesh Nishtala, Hans Fugal, Steven Grimm, Marc Kwiatkowski, Herman Lee, Harry C. Li, Ryan McElroy, Mike Paleczny, Daniel Peek, Paul Saab, David Stafford, Tony Tung, Venkateshwaran Venkataramani rajeshn,hans @fb.com, sgrimm, marc @facebook.com, herman, hcli, rm, mpal, dpeek, ps, dstaff, ttung, veeve @fb.com { } { } { } Facebook Inc. Abstract: Memcached is a well known, simple, in- however, web pages routinely fetch thousands of key- memory caching solution. This paper describes how value pairs from memcached servers. Facebook leverages memcached as a building block to One of our goals is to present the important themes construct and scale a distributed key-value store that that emerge at different scales of our deployment. While supports the world’s largest social network. Our system qualities like performance, efficiency, fault-tolerance, handles billions of requests per second and holds tril- and consistency are important at all scales, our experi- lions of items to deliver a rich experience for over a bil- ence indicates that at specific sizes some qualities re- lion users around the world. quire more effort to achieve than others. For exam- ple, maintaining data consistency can be easier at small 1 Introduction scales if replication is minimal compared to larger ones where replication is often necessary. Additionally, the Popular and engaging social networking sites present importance of finding an optimal communication sched- significant infrastructure challenges. Hundreds of mil- ule increases as the number of servers increase and net- lions of people use these networks every day and im- working becomes the bottleneck. pose computational, network, and I/O demands that tra- This paper includes four main contributions: (1) ditional web architectures struggle to satisfy. A social We describe the evolution of Facebook’s memcached- network’s infrastructure needs to (1) allow near real- based architecture. (2) We identify enhancements to time communication, (2) aggregate content on-the-fly memcached that improve performance and increase from multiple sources, (3) be able to access and update memory efficiency. (3) We highlight mechanisms that very popular shared content, and (4) scale to process improve our ability to operate our system at scale. (4) millions of user requests per second. We characterize the production workloads imposed on We describe how we improved the open source ver- our system. sion of memcached [14] and used it as a building block to construct a distributed key-value store for the largest so- 2 Overview cial network in the world. We discuss our journey scal- The following properties greatly influence our design. ing from a single cluster of servers to multiple geograph- First, users consume an order of magnitude more con- ically distributed clusters. To the best of our knowledge, tent than they create. This behavior results in a workload this system is the largest memcached installation in the dominated by fetching data and suggests that caching world, processing over a billion requests per second and can have significant advantages. Second, our read op- storing trillions of items. erations fetch data from a variety of sources such as This paper is the latest in a series of works that have MySQL databases, HDFS installations, and backend recognized the flexibility and utility of distributed key- services. This heterogeneity requires a flexible caching value stores [1, 2, 5, 6, 12, 14, 34, 36]. This paper fo- strategy able to store data from disparate sources. cuses on memcached—an open-source implementation Memcached provides a simple set of operations (set, of an in-memory hash table—as it provides low latency get, and delete) that makes it attractive as an elemen- access to a shared storage pool at low cost. These quali- tal component in a large-scale distributed system. The ties enable us to build data-intensive features that would open-source version we started with provides a single- otherwise be impractical. For example, a feature that machine in-memory hash table. In this paper, we discuss issues hundreds of database queries per page request how we took this basic building block, made it more ef- would likely never leave the prototype stage because it ficient, and used it to build a distributed key-value store would be too slow and expensive. In our application, that can process billions of requests per second. Hence- USENIX Association 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI ’13) 385 web web server server 1. get k 2. SELECT ... 1. UPDATE ... 2. delete k 3. set (k,v) memcache database memcache database Figure 1: Memcache as a demand-filled look-aside cache. The left half illustrates the read path for a web server on a cache miss. The right half illustrates the write path. forth, we use ‘memcached’ to refer to the source code or a running binary and ‘memcache’ to describe the dis- tributed system. Figure 2: Overall architecture memcache Query cache: We rely on to lighten the read We structure our paper to emphasize the themes that memcache load on our databases. In particular, we use emerge at three different deployment scales. Our read- demand-filled look-aside as a cache as shown in Fig- heavy workload and wide fan-out is the primary con- ure 1. When a web server needs data, it first requests cern when we have one cluster of servers. As it becomes memcache the value from by providing a string key. If necessary to scale to multiple frontend clusters, we ad- the item addressed by that key is not cached, the web dress data replication between these clusters. Finally, we server retrieves the data from the database or other back- describe mechanisms to provide a consistent user ex- end service and populates the cache with the key-value perience as we spread clusters around the world. Op- pair. For write requests, the web server issues SQL state- erational complexity and fault tolerance is important at ments to the database and then sends a delete request to all scales. We present salient data that supports our de- memcache that invalidates any stale data. We choose to sign decisions and refer the reader to work by Atikoglu delete cached data instead of updating it because deletes et al. [8] for a more detailed analysis of our workload. At Memcache are idempotent. is not the authoritative source a high-level, Figure 2 illustrates this final architecture in of the data and is therefore allowed to evict cached data. which we organize co-located clusters into a region and While there are several ways to address excessive designate a master region that provides a data stream to read traffic on MySQL databases, we chose to use keep non-master regions up-to-date. memcache. It was the best choice given limited engi- While evolving our system we prioritize two ma- neering resources and time. Additionally, separating our jor design goals. (1) Any change must impact a user- caching layer from our persistence layer allows us to ad- facing or operational issue. Optimizations that have lim- just each layer independently as our workload changes. ited scope are rarely considered. (2) We treat the prob- Generic cache: We also leverage memcache as a more ability of reading transient stale data as a parameter to general key-value store. For example, engineers use be tuned, similar to responsiveness. We are willing to memcache to store pre-computed results from sophisti- expose slightly stale data in exchange for insulating a cated machine learning algorithms which can then be backend storage service from excessive load. used by a variety of other applications. It takes little ef- fort for new services to leverage the existing marcher 3 In a Cluster: Latency and Load infrastructure without the burden of tuning, optimizing, We now consider the challenges of scaling to thousands provisioning, and maintaining a large server fleet. of servers within a cluster. At this scale, most of our As is, memcached provides no server-to-server co- efforts focus on reducing either the latency of fetching ordination; it is an in-memory hash table running on cached data or the load imposed due to a cache miss. a single server. In the remainder of this paper we de- scribe how we built a distributed key-value store based 3.1 Reducing Latency on memcached capable of operating under Facebook’s Whether a request for data results in a cache hit or miss, workload. Our system provides a suite of configu- the latency of memcache’s response is a critical factor ration, aggregation, and routing services to organize in the response time of a user’s request. A single user memcached instances into a distributed system. web request can often result in hundreds of individual 386 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI ’13) USENIX Association memcache get requests. For example, loading one of our popular pages results in an average of 521 distinct items UDP direct fetched from memcache.1 by mcrouter (TCP) We provision hundreds of memcached servers in a cluster to reduce load on databases and other services. Items are distributed across the memcached servers through consistent hashing [22]. Thus web servers have to routinely communicate with many memcached servers microseconds to satisfy a user request. As a result, all web servers communicate with every memcached server in a short period of time. This all-to-all communication pattern can cause incast congestion [30] or allow a single server 0 200 600 1000 1400 to become the bottleneck for many web servers. Data Average of Medians Average of 95th Percentiles replication often alleviates the single-server bottleneck but leads to significant memory inefficiencies in the Figure 3: Get latency for UDP, TCP via mcrouter common case.
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